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考虑到主动微波和被动光学遥感数据反映地表土壤水分的各自优势,提出一种ASAR数据和TM数据协同反演植被覆盖土壤水分的半经验耦合模型.该模型通过简化MIMICS模型,将研究对象分为植被冠层和土壤层两部分,模拟了冠层叶片含水量与单位体积内植被消光系数,后向/双向散射系数的经验关系,减少了模型的输入参数,使模型最关键的输入参数为光学易于反演的叶面积指数LAI.LAI采用PROSAIL模型进行反演,实现微波和光学模型的耦合,并引入植被均方根高度(Sveg)来修正冠层重叠造成的雷达阴影效应,然后对半经验模型的系数进行了参数敏感性分析,发现在LAI较小时(LAI≤3),模型更为适用.最后,选用甘肃黑河试验区的TM,ASAR数据,利用耦合模型生成了研究区土壤水分布图,并利用地面实测数据对该耦合模型和MIMICS模型进行比较验证.结果表明:通过对雷达阴影效应的校正,该模型反演的地表土壤水分与实测值的平均相对误差Er从17.6%减小到10.4%,RMS从0.055降低到0.031g/cm3.同时,耦合模型的反演效果明显好于MIMICS模型单独反演的结果(Er=22.7%,RMS=0.068g/cm3);表明在LAI较小的区域,该主被动遥感耦合模型能有效的反演土壤水分,取得较好的反演精度.
Considering the respective advantages of active microwave and passive optical remote sensing data to reflect the soil moisture on the surface, a semi-empirical coupling model of ASAR data and TM data for collaborative inversion of soil moisture under vegetation cover is proposed.This model simplifies the MIMICS model and divides the research object into Vegetation canopy and soil layer. The empirical relationship between the water content of canopy leaf and the extinction coefficient and backscatter / backscattering coefficient per unit volume was simulated, and the input parameters of the model were reduced. The most critical input parameters of the model were optical LAI.LAI, an easily invertible leaf area index, was inverted using the PROSAIL model to achieve coupling between microwave and optical models. The vegetation root mean square (Sveg) was introduced to correct the radar shadow effect caused by canopy overlap. The results show that the model is more suitable when the LAI is smaller (LAI≤3) .Finally, the TM and ASAR data of the Heihe experimental area in Gansu Province are used to generate the soil water distribution map , And the ground-based measured data are used to verify the coupling model and the MIMICS model.The results show that by correcting the shadow effect of radar, The average relative error Er between the retrieved soil moisture and the measured value decreased from 17.6% to 10.4% and the RMS decreased from 0.055 to 0.031g / cm3. Meanwhile, the inversion effect of the coupling model was significantly better than that of the MIMICS model alone (Er = 22.7%, RMS = 0.068g / cm3). The results show that in the area with small LAI, the active-passive coupled remote sensing model can effectively invert soil moisture and obtain better inversion accuracy.